Face Generation¶

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data¶

You'll be using two datasets in this project:

MNIST
CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)

Found mnist Data
Found celeba Data

Explore the Data¶

MNIST¶

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')

<matplotlib.image.AxesImage at 0x7f3a6a3079e8>

CelebA¶

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))

<matplotlib.image.AxesImage at 0x7f3a6a2335c0>

Preprocess the Data¶

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network¶

You'll build the components necessary to build a GANs by implementing the following functions below:

model_inputs
discriminator
generator
model_loss
model_opt
train

Check the Version of TensorFlow and Access to GPU¶

This will check to make sure you have the correct version of TensorFlow and access to a GPU

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))

TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input¶

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
Z input placeholder with rank 2 using z_dim.
Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function

    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32)
    
    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)

Tests Passed

Discriminator¶

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # alpha for leaky Relu
    alpha =0.2
    keep_prob = 0.5
    
    """ article recommendations:
    Architecture guidelines for stable Deep Convolutional GANs
     Replace any pooling layers with strided convolutions (discriminator) and fractional-strided
    convolutions (generator).
     Use batchnorm in both the generator and the discriminator.
        except generator output layer and the discriminator input layer.
     Remove fully connected hidden layers for deeper architectures.
     Use ReLU activation in generator for all layers except for the output, which uses Tanh.
     Use LeakyReLU activation in the discriminator for all layers."""
    
    """ Tips from review
    Using dropout in discriminator so that it is less prone to learning the data distribution.
    Use custom weight initialization. Xavier init is proposed to work best when working with GANs."""

    # TODO: Implement Function
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer is 28x28x3
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        relu1 = tf.maximum(alpha * x1, x1)
        # 14x14x64
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        # 7x7x128
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        # 4x4x256
        
        # dropout
        drop = tf.nn.dropout(relu3, keep_prob)

        # Flatten it
        flat = tf.reshape(drop, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
    return out, logits



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)

Tests Passed

Generator¶

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # alpha for leaky Relu
    alpha =0.1
        
    """ article recommendations:
    Architecture guidelines for stable Deep Convolutional GANs
     Replace any pooling layers with strided convolutions (discriminator) and fractional-strided
    convolutions (generator).
     Use batchnorm in both the generator and the discriminator.
        except generator output layer and the discriminator input layer.
     Remove fully connected hidden layers for deeper architectures.
     Use ReLU activation in generator for all layers except for the output, which uses Tanh.
     Use LeakyReLU activation in the discriminator for all layers."""
    
    """ Tips from review
    Use leaky_relu.
    Try setting leak for leaky_relu a bit lower. Did you tried 0.1 ?
    Try decreasing the width of layers from 256 -> 64. 
    In context of GANs, a sharp decline in number of filters for Generator helps produce better results."""
    
    # TODO: Implement Function
    with tf.variable_scope('generator', reuse=not(is_train)):
        # First fully connected layer
        x1 = tf.layers.dense(z, 7*7*256)

        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 256))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        #x1 = tf.nn.relu(x1)
        # 7x7 now
        
        x2 = tf.layers.conv2d_transpose(x1, 64, 4, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        #x2 = tf.nn.relu(x2)
        # 14x14 now
        
        x3 = tf.layers.conv2d_transpose(x2, 64, 4, strides=1, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        #x3 = tf.nn.relu(x3)
        # 14x14 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 4, strides=2, padding='same')
        # 28x28x3 now
        out = tf.tanh(logits)
        
    return out    


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)

Tests Passed

Loss¶

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)

def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """

    """ Tips from review
    Use One Sided Label Smoothing for Discriminator loss, will help it generalize better. 
    If you have two target labels: Real=1 and Fake=0, then for each incoming sample, 
    if it is real, then replace the label with a random number between 0.7 and 1.2, and 
    if it is a fake sample, replace it with 0.0 and 0.3 (for example).
    A simple change like labels = tf.ones_like(d_logits_real) * np.random.uniform(0.7, 1.2) 
    will help with the training process. This is known as label smoothing, typically used with 
    classifiers to improve performance.
    However, only-one-sided label smoothing is recommended to weaken the D and not G."""
    
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, 
                                                labels=tf.ones_like(d_model_real)* np.random.uniform(0.7, 1.2)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, 
                                                labels=tf.zeros_like(d_model_fake)* np.random.uniform(0.0, 0.3)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss    

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)

Tests Passed

Optimization¶

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt  

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)

Tests Passed

Neural Network Training¶

Show Output¶

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train¶

Implement train to build and train the GANs. Use the following functions you implemented:

model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    #sample_z = np.random.uniform(-1, 1, size=(72, z_dim))

    losses = []
    steps = 0
    print_every=20
    show_every = 100
    n_images=25
    
    # define channel dimension
    if(data_image_mode=="RGB"):
        out_channel_dim = 3
    else:
        out_channel_dim = 1
            
    image_width = data_shape[1]
    image_height = data_shape[2]
    image_channels = data_shape[3]
    
    print(data_shape)
    print(image_width)
    print(image_height)
    print(image_channels)

    # define generator and discriminator (create)
    inputs_real, inputs_z, learning_rate_tf = model_inputs(image_width, image_height, image_channels, z_dim)
    d_loss, g_loss = model_loss(inputs_real, inputs_z, out_channel_dim)
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate_tf, beta1)
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                
                # TODO: Train Model
                # normalize images between -1 and +1
                batch_images = batch_images *2
                
                steps += 1

                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_train_opt, feed_dict={inputs_real: batch_images, inputs_z: batch_z, learning_rate_tf:learning_rate})
                _ = sess.run(g_train_opt, feed_dict={inputs_real: batch_images,inputs_z: batch_z, learning_rate_tf:learning_rate})

                if steps % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = sess.run(d_loss, {inputs_z: batch_z, inputs_real: batch_images})
                    train_loss_g = g_loss.eval({inputs_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    # Save losses to view after training
                    losses.append((d_loss, g_loss))

                if steps % show_every == 0:
                    show_generator_output(sess, n_images, inputs_z, out_channel_dim, data_image_mode)

    return losses

MNIST¶

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

batch_size = 32
z_dim = 50
learning_rate = 0.0002
beta1 = 0.1


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)

(60000, 28, 28, 1)
28
28
1
Epoch 1/2... Discriminator Loss: 0.8376... Generator Loss: 2.2332
Epoch 1/2... Discriminator Loss: 1.2958... Generator Loss: 3.4788
Epoch 1/2... Discriminator Loss: 1.0696... Generator Loss: 1.4136
Epoch 1/2... Discriminator Loss: 1.4626... Generator Loss: 0.7559
Epoch 1/2... Discriminator Loss: 1.5103... Generator Loss: 0.5149

Epoch 1/2... Discriminator Loss: 1.8098... Generator Loss: 4.4629
Epoch 1/2... Discriminator Loss: 1.6056... Generator Loss: 0.7134
Epoch 1/2... Discriminator Loss: 1.0224... Generator Loss: 1.1718
Epoch 1/2... Discriminator Loss: 1.5635... Generator Loss: 0.7243
Epoch 1/2... Discriminator Loss: 1.0197... Generator Loss: 0.9144

Epoch 1/2... Discriminator Loss: 0.9542... Generator Loss: 1.4939
Epoch 1/2... Discriminator Loss: 1.7100... Generator Loss: 3.6442
Epoch 1/2... Discriminator Loss: 2.3653... Generator Loss: 5.1978
Epoch 1/2... Discriminator Loss: 1.1506... Generator Loss: 3.1884
Epoch 1/2... Discriminator Loss: 1.1251... Generator Loss: 0.8148

Epoch 1/2... Discriminator Loss: 1.3197... Generator Loss: 0.9075
Epoch 1/2... Discriminator Loss: 0.9767... Generator Loss: 2.1928
Epoch 1/2... Discriminator Loss: 0.9235... Generator Loss: 1.1388
Epoch 1/2... Discriminator Loss: 1.0716... Generator Loss: 2.9927
Epoch 1/2... Discriminator Loss: 0.8951... Generator Loss: 1.7008

Epoch 1/2... Discriminator Loss: 1.0290... Generator Loss: 1.0046
Epoch 1/2... Discriminator Loss: 1.0049... Generator Loss: 1.2232
Epoch 1/2... Discriminator Loss: 1.3536... Generator Loss: 0.7039
Epoch 1/2... Discriminator Loss: 1.1423... Generator Loss: 2.7731
Epoch 1/2... Discriminator Loss: 0.8797... Generator Loss: 2.3377

Epoch 1/2... Discriminator Loss: 0.9849... Generator Loss: 1.3175
Epoch 1/2... Discriminator Loss: 1.1823... Generator Loss: 3.5573
Epoch 1/2... Discriminator Loss: 1.1417... Generator Loss: 2.9647
Epoch 1/2... Discriminator Loss: 0.7757... Generator Loss: 2.1043
Epoch 1/2... Discriminator Loss: 1.3004... Generator Loss: 0.8090

Epoch 1/2... Discriminator Loss: 0.9994... Generator Loss: 1.2850
Epoch 1/2... Discriminator Loss: 1.7650... Generator Loss: 3.3184
Epoch 1/2... Discriminator Loss: 1.2134... Generator Loss: 2.5682
Epoch 1/2... Discriminator Loss: 0.7989... Generator Loss: 1.8954
Epoch 1/2... Discriminator Loss: 0.8870... Generator Loss: 1.3222

Epoch 1/2... Discriminator Loss: 0.8910... Generator Loss: 1.3294
Epoch 1/2... Discriminator Loss: 1.1957... Generator Loss: 2.7296
Epoch 1/2... Discriminator Loss: 1.0317... Generator Loss: 1.2105
Epoch 1/2... Discriminator Loss: 0.9409... Generator Loss: 1.9556
Epoch 1/2... Discriminator Loss: 1.0960... Generator Loss: 1.0428

Epoch 1/2... Discriminator Loss: 0.8171... Generator Loss: 1.2741
Epoch 1/2... Discriminator Loss: 1.2033... Generator Loss: 1.1544
Epoch 1/2... Discriminator Loss: 0.9669... Generator Loss: 1.3934
Epoch 1/2... Discriminator Loss: 1.0509... Generator Loss: 0.8522
Epoch 1/2... Discriminator Loss: 1.2607... Generator Loss: 0.8583

Epoch 1/2... Discriminator Loss: 1.1628... Generator Loss: 1.4377
Epoch 1/2... Discriminator Loss: 1.0536... Generator Loss: 2.0017
Epoch 1/2... Discriminator Loss: 1.1877... Generator Loss: 0.9857
Epoch 1/2... Discriminator Loss: 1.0100... Generator Loss: 1.2360
Epoch 1/2... Discriminator Loss: 1.3113... Generator Loss: 2.3586

CelebA¶

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

batch_size = 32
z_dim = 50
learning_rate = 0.0002
beta1 = 0.1


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)

(202599, 28, 28, 3)
28
28
3
Epoch 1/1... Discriminator Loss: 1.4094... Generator Loss: 1.0437
Epoch 1/1... Discriminator Loss: 0.8749... Generator Loss: 3.9864
Epoch 1/1... Discriminator Loss: 0.8485... Generator Loss: 1.7109
Epoch 1/1... Discriminator Loss: 1.8031... Generator Loss: 0.5662
Epoch 1/1... Discriminator Loss: 1.4180... Generator Loss: 1.0927

Epoch 1/1... Discriminator Loss: 1.2537... Generator Loss: 0.7484
Epoch 1/1... Discriminator Loss: 1.0033... Generator Loss: 1.3240
Epoch 1/1... Discriminator Loss: 0.9742... Generator Loss: 1.6777
Epoch 1/1... Discriminator Loss: 1.4503... Generator Loss: 0.8063
Epoch 1/1... Discriminator Loss: 0.9433... Generator Loss: 1.4892

Epoch 1/1... Discriminator Loss: 0.7768... Generator Loss: 1.9312
Epoch 1/1... Discriminator Loss: 1.2694... Generator Loss: 1.9368
Epoch 1/1... Discriminator Loss: 1.8215... Generator Loss: 0.5753
Epoch 1/1... Discriminator Loss: 1.5460... Generator Loss: 3.1456
Epoch 1/1... Discriminator Loss: 1.1845... Generator Loss: 2.2021

Epoch 1/1... Discriminator Loss: 1.8317... Generator Loss: 0.5067
Epoch 1/1... Discriminator Loss: 1.2186... Generator Loss: 0.9795
Epoch 1/1... Discriminator Loss: 1.3854... Generator Loss: 2.1959
Epoch 1/1... Discriminator Loss: 1.4305... Generator Loss: 0.7143
Epoch 1/1... Discriminator Loss: 1.7852... Generator Loss: 0.5216

Epoch 1/1... Discriminator Loss: 1.2803... Generator Loss: 1.7136
Epoch 1/1... Discriminator Loss: 1.3418... Generator Loss: 0.8625
Epoch 1/1... Discriminator Loss: 1.6330... Generator Loss: 0.4761
Epoch 1/1... Discriminator Loss: 1.1896... Generator Loss: 0.8036
Epoch 1/1... Discriminator Loss: 1.0163... Generator Loss: 1.0977

Epoch 1/1... Discriminator Loss: 1.2546... Generator Loss: 1.8575
Epoch 1/1... Discriminator Loss: 1.1758... Generator Loss: 1.4718
Epoch 1/1... Discriminator Loss: 1.2414... Generator Loss: 1.0668
Epoch 1/1... Discriminator Loss: 1.1867... Generator Loss: 1.7016
Epoch 1/1... Discriminator Loss: 1.1516... Generator Loss: 1.2993

Epoch 1/1... Discriminator Loss: 1.3018... Generator Loss: 1.3481
Epoch 1/1... Discriminator Loss: 1.1213... Generator Loss: 1.1371
Epoch 1/1... Discriminator Loss: 1.2953... Generator Loss: 2.1617
Epoch 1/1... Discriminator Loss: 1.2344... Generator Loss: 0.9921
Epoch 1/1... Discriminator Loss: 1.0084... Generator Loss: 1.1078

Epoch 1/1... Discriminator Loss: 1.1168... Generator Loss: 0.9802
Epoch 1/1... Discriminator Loss: 1.1623... Generator Loss: 0.9789
Epoch 1/1... Discriminator Loss: 1.1667... Generator Loss: 1.6282
Epoch 1/1... Discriminator Loss: 1.2355... Generator Loss: 0.9749
Epoch 1/1... Discriminator Loss: 1.2426... Generator Loss: 1.1368

Epoch 1/1... Discriminator Loss: 1.2733... Generator Loss: 0.8529
Epoch 1/1... Discriminator Loss: 1.1936... Generator Loss: 1.1260
Epoch 1/1... Discriminator Loss: 1.3931... Generator Loss: 0.7901
Epoch 1/1... Discriminator Loss: 1.3461... Generator Loss: 1.9424
Epoch 1/1... Discriminator Loss: 1.1279... Generator Loss: 1.0983

Epoch 1/1... Discriminator Loss: 1.2477... Generator Loss: 0.6921
Epoch 1/1... Discriminator Loss: 1.0978... Generator Loss: 1.0051
Epoch 1/1... Discriminator Loss: 1.2302... Generator Loss: 1.4927
Epoch 1/1... Discriminator Loss: 1.1993... Generator Loss: 1.2509
Epoch 1/1... Discriminator Loss: 1.2426... Generator Loss: 1.7799

Submitting This Project¶

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.